Bayesian model averaging and model selection for markov equivalence classes of acyclic digraphs

DOI10.1080/03610929608831853zbMath0894.62032OpenAlexW1971989560MaRDI QIDQ3125768

Michael D. Perlman, David Madigan, Chris T. Volinsky, Steen Arne Andersson

Publication date: 17 February 1998

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610929608831853

zbMATH Keywords

Markov chain Monte Carlomodel uncertaintymodel averagingMarkov equivalenceessential graphBayesian graphical model

Mathematics Subject Classification ID

Bayesian inference (62F15) Applications of graph theory (05C90) Foundations and philosophical topics in statistics (62A01)

Related Items

Gaussian Bayesian network comparisons with graph ordering unknown, Learning Markov equivalence classes of directed acyclic graphs: an objective Bayes approach, A characterization of Markov equivalence classes for acyclic digraphs, Learning dynamic causal relationships among sugar prices, Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs, Bayesian Model Selection of Gaussian Directed Acyclic Graph Structures, Complexity analysis of Bayesian learning of high-dimensional DAG models and their equivalence classes, Inferring gene regulatory networks by an order independent algorithm using incomplete data sets, Bayesian model averaging assessment on groundwater management under model structure uncertainty, Equivalence class selection of categorical graphical models, Parameter priors for directed acyclic graphical models and the characterization of several probability distributions, Characterizing Markov equivalence classes for AMP chain graph models, Uniform random generation of large acyclic digraphs, Separation and completeness properties for AMP chain graph Markov models., The size distribution for Markov equivalence classes of acyclic digraph models., Effective and efficient structure learning with pruning and model averaging strategies

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• BIFROST -- Block recursive models Induced From Relevant knowledge, Observations, and Statistical Techniques
• Causation, prediction, and search
• Hyper Markov laws in the statistical analysis of decomposable graphical models
• Bayesian analysis in expert systems. With comments and a rejoinder by the authors
• Markov chains for exploring posterior distributions. (With discussion)
• Sequential updating of conditional probabilities on directed graphical structures
• A fast procedure for model search in multidimensional contingency tables
• Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window
• Eliciting prior information to enhance the predictive performance of bayesian graphical models
• Independence properties of directed markov fields
• Birth Defects Registered by Double Sampling: A Bayesian Approach Incorporating Covariates and Model Uncertainty
• Bayesian Graphical Models for Discrete Data